
Arabic Online Commentary (AOC). This is a repository of 3M Arabic comments on online news sites developed by [1]}. AOC is labeled with MSA and the 3 regional dialects Egyptian, Gulf, and Levantine.

ArSarcasmDia. This dataset is developed by farha2020arabic for sarcasm detection but also carries regional dialect labels from the set {Egyptian, Gulf, Levantine, and Maghrebi}. ArSarcasmDia comprises \(10,547\)  tweets, \(2,472\)  of which are taken from ASTD [2]} while the rest (n=\(8,075\) ) come from SemEval 2017 [3]}.

MADAR. Sub-task 2 of the MADAR shared task [4]}https://sites.google.com/view/madar-shared-task/home. is focused on user-level dialect identification with country labels. The dataset is composed of \(\sim 265\) K manually-curated Tweets. In this paper, we evaluate our models on MADAR at the tweet level and compare to the shared task top ranking system's [5]} tweet-level results.

NADI. The first Nuanced Arabic Dialect Identification shared task (NADI 2020) [6]}https://sites.google.com/view/nadi-shared-task/home. targets country level as well as province level dialects. The dataset is composed of \(21,000\)  tweets, covering 21 Arab countries and 100 respective provinces.

QADI. The QCRI Arabic Dialect Identification (QADI) dataset [7]} is labeled at the country level and has 540K tweets from \(2,525\)  Twitter users.

